Error correcting codes encode messages by codewords in such a way that even if some of the codeword is corrupted, the message can be decoded. Typical decoding algorithms for error correcting codes either use linear space or quadratic time. A natural question is whether codes can be decoded in near-linear time and sub-linear space simultaneously. A recent result by Cook and Moshkovitz gave efficient decoders that can uniquely decode Reed-Muller and other codes from a constant fraction (less than half) of corruption. In this work, we address the problem of list decoding in near-linear time and sub-linear space. In the list decoding setting, most of the codeword is corrupted, and one wants to output a short list of potential messages that contains the true message. For any constants γ, τ > 0, we give decoders for Reed-Muller codes that can decode from 1-γ fraction of corruptions in time n^{1+τ} and space n^{τ}. Our decoders work by extending the iterative correction technique of Cook and Moshkovitz. However, that technique, which gradually decreases the number of corruptions in the message, was tailored to the unique decoding setting. We first identify an intermediate problem, codewords list recovery, for which we can make iterative correction work. We then show how to reduce general list decoding to the codewords list recovery problem in efficient time and space. The reduction relies on local correction and testing. In the codewords list recovery problem, the input consists of n unordered lists containing exactly the symbols from L codewords, where a small fraction of the lists is corrupted. The goal is to find the L codewords. In addition, we prove that any linear code with time-space efficient encoding or decoding must be local, in the sense that the codewords satisfy a local linear constraint. This rules out codes like Reed-Solomon from having time-space efficient encoding or decoding.

We point out an error in the paper "Linear Time Encoding of LDPC Codes" (by Jin Lu and José M. F. Moura, IEEE Trans). The paper claims to present a linear time encoding algorithm for every LDPC code. We present a family of counterexamples, and point out where the analysis fails. The algorithm in the aforementioned paper fails to encode our counterexample, let alone in linear time.

We present simple constructions of good approxi-mate locally decodable codes (ALDCs) in the presence of a$\delta{-}$fraction of errors for$\delta < 1/2$. In a standard locally decodable code$C:\Sigma_{1}^{k}\rightarrow\Sigma_{2}^{n}$, there is a decoder$M$that on input$i\in[k]$correctly outputs the i-th symbol of a message$x$(with high probability) using only$q$queries to a given string$w$that is$\Delta$-close to$C(x)$. In an ALDC, the decoder$M$only needs to be correct on a 1$-\varepsilon$fraction of$i\in[k]$for$\varepsilon$much smaller than$\delta$. We present a construction of explicit ALDCs for all constants 1/2$> \delta > \varepsilon$with a constant number of queries$q$and with constant, near-optimal rate. Standard LDCs with constant number of queries and any constant rate are known to be impossible. We additionally explore what is the lowest error probability$\varepsilon$one can achieve for fixed$\delta$and$q$. We show that for any ALDC,$\in=\Omega(\delta \mathrm{r}q/2\rceil)$. We then show that there exist explicit constant rate ALDCs for any constant$q$that achieve$\varepsilon=O(\delta^{\lceil q/2\rceil})$. In particular, for$q=3$, we have a constant rate ALDC with error probability$\varepsilon=O(\delta^{2})$. A full version of this paper is available at https://eccc.weizmann.ac.il/report/2023/056/.

A Chor–Goldreich (CG) source is a sequence of random variables X = X1 ∘ … ∘ Xt, where each Xi ∼ {0,1}d and Xi has δ d min-entropy conditioned on any fixing of X1 ∘ … ∘ Xi−1. The parameter 0<δ≤ 1 is the entropy rate of the source. We typically think of d as constant and t as growing. We extend this notion in several ways, defining almost CG sources. Most notably, we allow each Xi to only have conditional Shannon entropy δ d.

We reduce the problem of proving a "Boolean Unique Games Conjecture" (with gap 1-delta vs. 1-C*delta, for any C> 1, and sufficiently small delta>0) to the problem of proving a PCP Theorem for a certain non-unique game. In a previous work, Khot and Moshkovitz suggested an inefficient candidate reduction (i.e., without a proof of soundness). The current work is the first to provide an efficient reduction along with a proof of soundness. The non-unique game we reduce from is similar to non-unique games for which PCP theorems are known. Our proof relies on a new concentration theorem for functions in Gaussian space that are restricted to a random hyperplane. We bound the typical Euclidean distance between the low degree part of the restriction of the function to the hyperplane and the restriction to the hyperplane of the low degree part of the function.

Existing proofs that deduce BPP = P from circuit lower bounds convert randomized algorithms into deterministic algorithms with a large polynomial slowdown. We convert randomized algorithms into deterministic ones with little slowdown . Specifically, assuming exponential lower bounds against randomized NP ∩ coNP circuits, formally known as randomized SVN circuits, we convert any randomized algorithm over inputs of length n running in time t ≥ n into a deterministic one running in time t 2+α for an arbitrarily small constant α > 0. Such a slowdown is nearly optimal for t close to n , since under standard complexity-theoretic assumptions, there are problems with an inherent quadratic derandomization slowdown. We also convert any randomized algorithm that errs rarely into a deterministic algorithm having a similar running time (with pre-processing). The latter derandomization result holds under weaker assumptions, of exponential lower bounds against deterministic SVN circuits. Our results follow from a new, nearly optimal, explicit pseudorandom generator fooling circuits of size s with seed length (1+α)log s , under the assumption that there exists a function f ∈ E that requires randomized SVN circuits of size at least 2 (1-α′) n , where α = O (α)′. The construction uses, among other ideas, a new connection between pseudoentropy generators and locally list recoverable codes.

Existing proofs that deduce BPP = P from circuit lower bounds convert randomized algorithms into deterministic algorithms with a large polynomial slowdown. We convert randomized algorithms into deterministic ones with little slowdown. Specifically, assuming exponential lower bounds against randomized single-valued nondeterministic (SVN) circuits, we convert any randomized algorithm over inputs of length n running in time t ≥ n to a deterministic one running in time t2+αfor an arbitrarily small constant . Such a slowdown is nearly optimal, as, under complexity-theoretic assumptions, there are problems with an inherent quadratic derandomization slowdown. We also convert any randomized algorithm that errs rarely into a deterministic algorithm having a similar running time (with pre-processing). The latter derandomization result holds under weaker assumptions, of exponential lower bounds against deterministic SVN circuits. Our results follow from a new, nearly optimal, explicit pseudorandom generator fooling circuits of size s with seed length (1 + α)log s, under the assumption that there exists a function f ϵ E that requires randomized SVN circuits of size at least 2(1-α')n, where. α=O(α'). The construction uses, among other ideas, a new connection between pseudoentropy generators and locally list recoverable codes.

The long code is a central tool in hardness of approximation, especially in questions related to the unique games conjecture. We construct a new code that is exponentially more efficient, but can still be used in many of these applications. Using the new code we obtain exponential improvements over several known results, including the following: 1. For any eps > 0, we show the existence of an n vertex graph G where every set of o(n) vertices has expansion 1 - eps, but G's adjacency matrix has more than exp(log^delta n) eigenvalues larger than 1 - eps, where delta depends only on eps. This answers an open question of Arora, Barak and Steurer (FOCS 2010) who asked whether one can improve over the noise graph on the Boolean hypercube that has poly(log n) such eigenvalues. 2. A gadget that reduces unique games instances with linear constraints modulo K into instances with alphabet k with a blowup of K^polylog(K), improving over the previously known gadget with blowup of 2^K. 3. An n variable integrality gap for Unique Games that that survives exp(poly(log log n)) rounds of the SDP + Sherali Adams hierarchy, improving on the previously known bound of poly(log log n). We show a connection between the local testability of linear codes and small set expansion in certain related Cayley graphs, and use this connection to derandomize the noise graph on the Boolean hypercube.

With any hypothesis class one can associate a bipartite graph whose vertices are the hypotheses H on one side and all possible labeled examples X on the other side, and an hypothesis is connected to all the labeled examples that are consistent with it. We call this graph the hypotheses graph. We prove that any hypothesis class whose hypotheses graph is mixing cannot be learned using less than Omega(log^2 |H|) memory bits unless the learner uses at least a large number |H|^Omega(1) labeled examples. Our work builds on a combinatorial framework that we suggested in a previous work for proving lower bounds on space bounded learning. The strong lower bound is obtained by defining a new notion of pseudorandomness, the entropy sampler. Raz obtained a similar result using different ideas.

One can learn any hypothesis class H with O(log |H|) labeled examples. Alas, learning with so few examples requires saving the examples in memory, and this requires |X|^(O(log|H|)) memory states, where X is the set of all labeled examples. This motivates the question of how many labeled examples are needed in case the memory is bounded. Previous work showed, using techniques such as linear algebra and Fourier analysis, that parities cannot be learned with bounded memory and less than |H|^(Omega(1)) examples. One might wonder whether a general combinatorial condition exists for unlearnability with bounded memory, as we have with the condition VCdim(H) = Infinity for PAC unlearnability. In this paper we give such a condition. We show that if an hypothesis class H, when viewed as a bipartite graph between hypotheses H and labeled examples X, is mixing, then learning it requires |H|^(Omega(1)) examples under a certain bound on the memory. Note that the class of parities is mixing. Moreover, as an immediate corollary, we get that most hypothesis classes are unlearnable with bounded memory. Our proof technique is combinatorial in nature and very different from previous analyses.

Many known optimal NP-hardness of approximation results are reductions from a problem called Label Cover. The input is a bipartite graph G = (L, R, E) and each edge e = (x,y) ∊ E carries a projection π∊ that maps labels to x to labels to y. The objective is to find a labeling of the vertices that satisfies as many of the projections as possible. It is believed that the best approximation ratio efficiently achievable for Label-Cover is of the form N−c where N = nk, n is the number of vertices, k is the number of labels, and 0 ≤ c < 1 is some constant. Inspired by a framework originally developed for Densest k-SuBGRAPH, we propose a “log density threshold” for the approximability of Label-Cover. Specifically, we suggest the possibility that the Label-Cover approximation problem undergoes a computational phase transition at the same threshold at which local algorithms for its random counterpart fail. This threshold is We then design, for any ∊ > 0, a polynomial-time approximation algorithm for semirandom Label-Cover whose approximation ratio is In our semi-random model, the input graph is random (or even just expanding), and the projections on the edges are arbitrary. For worst-case Label-Cover we show a polynomial- time algorithm whose approximation ratio is roughly Ν-°·233. The previous best efficient approximation ratio was Ν−0·25. We present some evidence towards an Ν−c threshold by constructing integrality gaps for Νω(1) rounds of the Sum-of-squares/Lasserre hierarchy of the natural relaxation of Label Cover. For general 2CSP the “log density threshold” is Ν−0·25, and we give a polynomial-time algorithm in the semi-random model whose approximation ratio is Ν−0·25+∊ for any ∊ > 0.

In this work we show a barrier towards proving a randomness-efficient parallel repetition, a promising avenue for achieving many tight inapproximability results. Feige and Kilian (STOC'95) proved an impossibility result for randomness-efficient parallel repetition for two prover games with small degree, i.e., when each prover has only few possibilities for the question of the other prover. In recent years, there have been indications that randomness-efficient parallel repetition (also called derandomized parallel repetition) might be possible for games with large degree, circumventing the impossibility result of Feige and Kilian. In particular, Dinur and Meir (CCC'11) construct games with large degree whose repetition can be derandomized using a theorem of Impagliazzo, Kabanets and Wigderson (SICOMP'12). However, obtaining derandomized parallel repetition theorems that would yield optimal inapproximability results has remained elusive. This paper presents an explanation for the current impasse in progress, by proving a limitation on derandomized parallel repetition. We formalize two properties which we call "fortification-friendliness" and "yields robust embeddings." We show that any proof of derandomized parallel repetition achieving almost-linear blow-up cannot both (a) be fortification-friendly and (b) yield robust embeddings. Unlike Feige and Kilian, we do not require the small degree assumption. Given that virtually all existing proofs of parallel repetition, including the derandomized parallel repetition result of Dinur and Meir, share these two properties, our no-go theorem highlights a major barrier to achieving almost-linear derandomized parallel repetition.

We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (nonuniform) algorithms. Unlike most existing techniques that involve repetition of the randomized algorithm and hence a slowdown, our techniques produce algorithms with similar run-time to the original randomized algorithms. The amplification technique applies when there is a quick, probabilistic test of the randomness. In this case, we show how to efficiently find one randomness string for the algorithm that works with very high probability and apply the algorithm only on that randomness (in contrast, standard approaches suggest a number of possible randomness strings and run the algorithm on all of them). The search of good randomness turns out to be a natural stochastic multiarmed bandit problem, which we define (``the biased coin problem'') and analyze. The derandomization technique applies when there is a verifier that can test the randomness of the algorithm while only inspecting a sublinear size sketch of the input (the sketch may be hard to compute; the verifier may be inefficient and is allowed to reject a small portion of the good randomness strings). In this case, we show how to apply Adleman's derandomization (from the proof of $BPP\subseteq P/poly$) more efficiently. We demonstrate the techniques by showing applications for dense max-cut, approximate clique, free games, and going from list decoding to unique decoding for Reed--Muller codes.

We propose a candidate reduction for ruling out polynomial-time algorithms for unique games, either under plausible complexity assumptions, or unconditionally for Lasserre semi-definite programs with a constant number of rounds. We analyze the completeness and Lasserre solution of our construction, and provide a soundness analysis in a certain setting of interest. Addressing general settings is tightly connected to a question on Gaussian isoperimetry. Our construction is based on our previous work on the complexity of approximately solving a system of linear equations over reals, which we suggested as an avenue towards a (positive) resolution of the Unique Games Conjecture. The construction employs a new encoding scheme that we call the real code. The real code has two useful properties: like the long code, it has a unique local test, and like the Hadamard code, it has the so-called sub-code covering property.

In this paper, we present a polynomial-time algorithm that approximates sufficiently high-value Max 2-CSPs on sufficiently dense graphs to within O(N^epsilon) approximation ratio for any constant epsilon > 0. Using this algorithm, we also achieve similar results for free games, projection games on sufficiently dense random graphs, and the Densest k-Subgraph problem with sufficiently dense optimal solution. Note, however, that algorithms with similar guarantees to the last algorithm were in fact discovered prior to our work by Feige et al. and Suzuki and Tokuyama. In addition, our idea for the above algorithms yields the following by-product: a quasi-polynomial time approximation scheme (QPTAS) for satisfiable dense Max 2-CSPs with better running time than the known algorithms.

We introduce and study a new model of interactive proofs: AM(k), or Arthur-Merlin with k non-communicating Merlins. Unlike with the better-known MIP, here the assumption is that each Merlin receives an independent random challenge from Arthur. One motivation for this model (which we explore in detail) comes from the close analogies between it and the quantum complexity class QMA(k), but the AM(k) model is also natural in its own right. We illustrate the power of multiple Merlins by giving an AM(2) protocol for 3SAT, in which the Merlins' challenges and responses consist of only n1/2+o(1)bits each. Our protocol has the consequence that, assuming the Exponential Time Hypothesis (ETH), any algorithm for approximating a dense CSP with a polynomial-size alphabet must take n(log n)1-o(1)time. Algorithms nearly matching this lower bound are known, but their running times had never been previously explained. Brandao and Harrow have also recently used our 3SAT protocol to show quasipolynomial hardness for approximating the values of certain entangled games. In the other direction, we give a simple quasipolynomial-time approximation algorithm for free games, and use it to prove that, assuming the ETH, our 3SAT protocol is essentially optimal. More generally, we show that multiple Merlins never provide more than a polynomial advantage over one: that is, AM(k) = AM for all k=poly(n). The key to this result is a sub sampling theorem for free games, which follows from powerful results by Alon et al. And Barak et al. On sub sampling dense CSPs, and which says that the value of any free game can be closely approximated by the value of a logarithmic-sized random subgame.

The Parallel Repetition Theorem upper-bounds the value of a repeated (tensored) two prover game in terms of the value of the base game and the number of repetitions. In this work we give a simple transformation on games -- "fortification" -- and show that for fortified games, the value of the repeated game decreases perfectly exponentially with the number of repetitions, up to an arbitrarily small additive error. Our proof is combinatorial and short. As corollaries, we obtain: (1) Starting from a PCP Theorem with soundness error bounded away from 1, we get a PCP with arbitrarily small constant soundness error. In particular, starting with the combinatorial PCP of Dinur, we get a combinatorial PCP with low error. The latter can be used for hardness of approximation as in the work of Håstad. (2) Starting from the work of the author and Raz, we get a projection PCP theorem with the smallest soundness error known today. The theorem yields nearly a quadratic improvement in the size compared to previous work. We then discuss the problem of derandomizing parallel repetition, and the limitations of the fortification idea in this setting. We point out a connection between the problem of derandomizing parallel repetition and the problem of composition. This connection could shed light on the so-called Projection Games Conjecture, which asks for projection PCP with minimal error.

In this paper we consider the additive white Gaussian noise channel with an average input power constraint in the power-limited regime. A well-known result in information theory states that the capacity of this channel can be achieved by random Gaussian coding with analog quadrature amplitude modulation (QAM). In practical applications, however, discrete binary channel codes with digital modulation are most often employed. We analyze the matched filter decoding error probability in random binary and Gaussian coding setups in the wide bandwidth regime, and show that the performance in the two cases is surprisingly similar without explicit adaptation of the codeword construction to the modulation. The result also holds for the multiple access and the broadcast Gaussian channels, when signal-to-noise ratio is low. Moreover, the two modulations can be even mixed together in a single codeword resulting in a hybrid modulation with asymptotically close decoding behavior. In this sense the matched filter decoder demonstrates the performance that is largely insensitive to the choice of binary versus Gaussian modulation.

In this paper, we consider the problem of approximately solving a system of homogeneous linear equations over reals, where each equation contains at most three variables. Since the all-zero assignment always satisfies all the equations exactly, we restrict the assignments to be “nontrivial.” Here is an informal statement of our result: it is $\mathcal{NP}$-hard to distinguish whether there is a nontrivial assignment that satisfies $1-\delta$ fraction of the equations or every nontrivial assignment fails to satisfy a constant fraction of the equations with a “margin” of $\Omega(\sqrt{\delta})$. We develop linearity and dictatorship testing procedures for functions $f: \mathbb{R}^n \mapsto \mathbb{R}$ over a Gaussian space, which could be of independent interest. Our research is motivated by a possible approach to proving the unique games conjecture.

In this paper, we consider the problem of approximately solving a system of homogeneous linear equations over reals, where each equation contains at most three variables.

This is an errata for our STOC'06 paper, "On Basing One-Way Functions on NP-Hardness".

We show that the NP-Complete language 3Sat has a PCPverifier that makes two queries to a proof of almost-linear size and achieves sub-constant probability of error o(1). The verifier performs only projection tests, meaning that the answer to the first query determines at most one accepting answer to the second query.Previously, by the parallel repetition theorem, there were PCP Theorems with two-query projection tests, but only (arbitrarily small) constant error and polynomial size.There were also PCP Theorems with sub-constant error andalmost-linear size, but a constant number of queries that is larger than 2.As a corollary, we obtain a host of new results. In particular, our theorem improves many of the hardness of approximation results that are proved using the parallel repetition theorem. A partial list includes the following:(1) 3Sat cannot be efficiently approximated to withina factor of 7/8+o(1), unless P = NP. This holds even under almost-linear reductions. Previously, the best knownNP-hardness factor was 7/8+epsilon for any constant epsilonGt0, under polynomial reductions.(2) 3Lin cannot be efficiently approximated to withina factor of 1/2+o(1), unless P = NP. This holdseven under almost-linear reductions. Previously, the best known NP-hardness factor was 1/2+epsilon for any constant epsilonGt0, under polynomial reductions.(3) A PCP Theorem with amortized query complexity 1 + o(1)and amortized free bit complexity o(1). Previously, the best known amortized query complexity and free bit complexity were 1+epsilon and epsilon, respectively, for any constant epsilon Gt 0.One of the new ideas that we use is a new technique for doing the composition step in the (classical) proof of the PCP Theorem, without increasing the number of queries to the proof. We formalize this as a composition of new objects that we call Locally Decode/Reject Codes (LDRC). The notion of LDRC was implicit in several previous works, and we make it explicit in this work. We believe that the formulation of LDRCs and their construction are of independent interest.

Given (the table of) a function $f : \mathbb{F}^m \rightarrow \mathbb{F}$ over a finite field $\mathbb{F}$, a low degree tester tests its agreement with an m-variate polynomial of total degree at most d over $\mathbb{F}$. The tester is usually given access to an oracle $\mathcal{A}$ providing the supposed restrictions of f to affine subspaces of constant dimension (e.g., lines, planes, etc.). The tester makes very few (probabilistic) queries to f and to $\mathcal{A}$ (say, one query to f and one query to $\mathcal{A}$) and decides whether to accept or reject based on the replies. We wish to minimize two parameters of the tester: its error and its size. The error bounds the probability that the tester accepts although the function is far from a low degree polynomial. The size is the number of bits required to write the oracle replies on all possible tester queries. Low degree testing is a central ingredient in most constructions of probabilistically checkable proofs (PCPs). The error of the low degree tester is related to the error of the PCP, and its size is related to the size of the PCP. We design and analyze new low degree testers that have both subconstant error $o(1)$ and almost-linear size $n^{1+o(1)}$ (where $n = \left|\mathbb{F}\right|^{m}$). Previous constructions of subconstant error testers had polynomial size. These testers enabled the construction of PCPs with subconstant error, but polynomial size. Previous constructions of almost-linear size testers obtained only constant error. These testers were used to construct almost-linear size PCPs with constant error. The testers we present in this work enabled the construction of PCPs with both subconstant error and almost-linear size.

We consider the possibility of basing one-way functions on NP-Hardness; that is, we study possible reductions from a worst-case decision problem to the task of average-case inverting a polynomial-time computable function f. Our main findings are the following two negative results:

This work addresses k-restriction problems , which unify combinatorial problems of the following type: The goal is to construct a short list of strings in Σ m that satisfies a given set of k -wise demands. For every k positions and every demand, there must be at least one string in the list that satisfies the demand at these positions. Problems of this form frequently arise in different fields in Computer Science.The standard approach for deterministically solving such problems is via almost k -wise independence or k -wise approximations for other distributions. We offer a generic algorithmic method that yields considerably smaller constructions. To this end, we generalize a previous work of Naor et al. [1995]. Among other results, we enhance the combinatorial objects in the heart of their method, called splitters, and construct multi-way splitters , using a new discrete version of the topological Necklace Splitting Theorem [Alon 1987].We utilize our methods to show improved constructions for group testing [Ngo and Du 2000] and generalized hashing [Alon et al. 2003], and an improved inapproximability result for SET-COVER under the assumption P ≠ NP .